(1) Field of Invention
The present invention relates to an instability detection system and, more particularly, to a system, method, and computer program product for predicting system instability.
(2) Description of Related Art
The present invention is directed to the field of system stability and change based on coordinated behavior patterns. There are many examples of complex networks showing coordinated behavior. The ability to predict system instability based on such behavior can provide crucial insight and time to address the predicted instability. For example, ecosystems and human society go through sudden regime shifts; these kinds of systematic shifts pose challenges to maintaining the stability of the nature or human society. Sudden disappearance of natural species could cause further disruption in the ecosystem. As another example, unexpected opinion swings or market collapse poses hard challenges to governing authorities. As a physiological example, early detection of brain seizure can provide considerable benefit to the affected patents. Efforts to detect and respond to such events before the onset of transitions are especially beneficial, because the measures to deal with any undesirable changes can be more effective before the system's full evolution toward a highly nonlinear system (see, for example, the List of Incorporated Cited Literature References, Literature Reference No. 5).
It has been speculated that catastrophic changes in nature are often preceded by peculiar signs, such as regular-shaped patches of vegetation before desertification (see Literature Reference Nos. 1, 2, and 3). Other researchers investigated catastrophic population changes observed in ecosystems, and derived quantitative indicators—increased temporal correlation, skewness, and spatial correlations of the population dynamics (see Literature Reference Nos. 4, 6, and 7). Fernandez et al. studied large-scale dynamical systems going through a bifurcation revealing self-organized spatial patterns as early signs (see Literature Reference No. 8). These approaches consider only the homogeneous lattice as the model of interactions. It would be a natural next step to investigate the coordinated behavior of evolving complex networks—consisting of a large number of entities exchanging heterogeneous influences that can model a wide range of real world systems.
Despite its broad applicability, however, the model of a heterogeneously networked dynamical system going through a phase transition has not been treated properly. The formidable issue of the complexity of connectivity has been mainly handled by statistical approaches—degree distributions, random network models (see Literature Reference No. 22, 23, and 24). Order emergence due to spin dynamics (see Literature Reference No. 9) or synchronization dynamics (see Literature Reference Nos. 10 and 11) has been extensively studied without considering the evolving connectivity and phase transition at the same time.
The idea of analyzing the data from complex networks having heterogeneous connectivity to quantitatively forecasting critical transition and estimating the network connectivity is completely foreign to the listed prior arts. Thus, a continuing need exists for a system that is capable of systematically analyzing multi-dimensional time series data from complex network dynamics to make forecasts of instabilities and critical transitions and estimate the network's underlying connectivity.